Incremental Application Programming Interface (API) Processing Based on Resource Utilization

ABSTRACT

System and methods are described for receiving a request from a client application to obtain data from a server; collecting a list of tasks to be performed by the server to process the request to obtain the data; performing tasks from the list of tasks by the server until an elapsed time to perform the list of tasks exceeds a first threshold and a size of a payload storing the data exceeds a second threshold; preparing the payload; and sending the payload to the client application.

TECHNICAL FIELD

One or more implementations relate to cloud computing environments, and more specifically to incremental processing of requests by mobile applications in a distributed system of a cloud computing environment.

BACKGROUND

“Cloud computing” services provide shared resources, software, and information to computers and other devices upon request or on demand. Cloud computing typically involves the over-the-Internet provision of dynamically scalable and often virtualized resources. Technological details can be abstracted from end-users, who no longer have need for expertise in, or control over, the technology infrastructure “in the cloud” that supports them. In cloud computing environments, software applications can be accessible over the Internet rather than installed locally on personal or in-house computer systems. Some of the applications or on-demand services provided to end-users can include the ability for a user to create, view, modify, store and share documents and other files.

In some situations, requests by end-users to obtain data from “the cloud” may be slowed down, interrupted, or even canceled when the requested data is large, cloud computing services are overloaded, and/or network access is unreliable.

BRIEF DESCRIPTION OF THE DRAWINGS

The included drawings are for illustrative purposes and serve to provide examples of possible structures and operations for the disclosed inventive systems, apparatus, methods, and computer-readable storage media. These drawings in no way limit any changes in form and detail that may be made by one skilled in the art without departing from the spirit and scope of the disclosed implementations.

FIG. 1A illustrates an example computing environment of an on-demand database service according to some embodiments.

FIG. 1B illustrates example implementations of elements of FIG. 1A and example interconnections between these elements according to some embodiments.

FIG. 2A illustrates example architectural components of an on-demand database service environment according to some embodiments.

FIG. 2B illustrates example architectural components of an on-demand database service environment according to some embodiments.

FIG. 3 is a diagrammatic representation of a machine in the exemplary form of a computer system within which one or more embodiments may be carried out.

FIG. 4 is a diagram of an example system for incremental application programming interface (API) processing based on resource utilization according to some embodiments.

FIG. 5 is a flow diagram of incremental API processing based on resource utilization according to some embodiments.

DETAILED DESCRIPTION

Embodiments of the present invention comprise a method and apparatus to receive a request from a user of a mobile client application in a cloud computing environment, generate the response to the request in an incremental way by cloud computing resources, taking into account current server utilization of the cloud, and send the response back to the user. Embodiments perform incremental processing of a large API request based not only on the size of the payload (e.g., the data being returned in response to the request), but on an adaptive model measuring server utilization, hyper-text transport protocol (HTTP) timeout scenarios, and different client needs in terms of response time. Embodiments produce different sets of resulting data depending on the current availability of server resources. This approach avoids inefficiencies that may occur when returning sets of data based on static criteria (such as the maximum size of the data returned, for example). Instead, embodiments adapt the response based at least in part on server utilization such as central processing unit (CPU) time, database (DB) access time, object query language (OQL) efficiencies, data size in a database for requested data, etc.

In one embodiment, a mobile client application is a consumer of an API endpoint named “Priming Records.” In an embodiment, a workload and/or a container running the workload in a server of a cloud computing environment is known as an endpoint. One purpose of the Priming Records endpoint is to obtain and return a set of data records that are being requested by the mobile client application. The endpoint performs the requested retrieval operation as collection of individual tasks, each of which perform distinct database operations (e.g., structured query language (SQL) operations). Although in one embodiment the request is to query a database, in other embodiments and other computing scenarios the request may represent any processing including input/output (I/O) processing, such as network calls, synchronous queue processing, and so on.

In some embodiments, the individual tasks resulting from the user request can number up to 100, and each of the results from the tasks can be up to 2,000 records, with the API endpoint thus evaluating up to a maximum of 200,000 records. Due to this large size, the API endpoint consumes significant server processing capability depending on the current load on the server resources (e.g., database accesses and CPU load). In mobile client applications, the probability of error scenarios increases with API payload size and request processing times. The problem is exacerbated when the mobile device running the mobile client application is used when the network access to the cloud computing environment is unreliable.

In one approach to this problem, the server splits API processing from a single request into multiple requests based on the number of tasks (e.g., SQL processing) needed to complete the API processing for the entire request. One problem with this approach is that the number of communications between the mobile client application on the mobile device and the server in the cloud computing environment increases drastically, even when each of the tasks is trivial and could be accommodated within a single request. The increased communications load often results in errors and poor performance being experienced by the user.

In another approach as described herein, an adaptive model (called “Relay-based Priming” herein) is applied wherein a plurality of processing units (e.g., endpoints) in the cloud computing environment processes a portion of each request, taking into account a fixed maximum processing time per request and a fixed maximum payload size that will be returned in response to the request. This adaptive model takes into consideration the potential timeout scenarios that may happen when a certain threshold (e.g., a configured value) is reached, the processing terminates when the elapsed time at the API level approaches these limits. This adaptive solution of embodiments of the present invention provides the advantage of dynamically consuming more or less server resources, based on server occupancy. When the server is servicing the endpoint during off-peak hours, the number of task items (e.g., SQL units) that can be processed is higher and hence the mobile client application may receive a bigger payload (within a threshold), whereas when the server resources are scant during peak hours (since the processing time may also increase due to unavailability of resources (e.g., DB connection pools)), the number of processing units per request correspondingly decreases, and the mobile client application may receive a smaller payload. This adaptive solution reduces potential error occurrences by adapting API request processing to current server availability and by ensuring the payload size is not exceeded in a single request call.

FIG. 1A illustrates a block diagram of an example of a cloud computing environment 10 in which an on-demand database service can be used in accordance with some implementations. Environment 10 includes user systems 12 (e.g., customer's computing systems), a network 14, a database system 16 (also referred to herein as a “cloud-based system” or a “cloud computing system”), a processing device 17, an application platform 18, a network interface 20, a tenant database 22 for storing tenant data (such as data sets), a system database 24 for storing system data, program code 26 for implementing various functions of the database system 16 (including a visual data cleaning application), and process space 28 for executing database system processes and tenant-specific processes, such as running applications for customers as part of an application hosting service. In some other implementations, environment 10 may not have all these components or systems, or may have other components or systems instead of, or in addition to, those listed above. In some embodiments, tenant database 22 is a shared storage.

In some implementations, environment 10 is a computing environment in which an on-demand database service (such as a distributed search application) exists. An on-demand database service, such as that which can be implemented using database system 16, is a service that is made available to users outside an enterprise (or enterprises) that owns, maintains, or provides access to database system 16. As described above, such users generally do not need to be concerned with building or maintaining database system 16. Instead, resources provided by database system 16 may be available for such users' use when the users need services provided by database system 16; that is, on the demand of the users. Some on-demand database services can store information from one or more tenants into tables of a common database image to form a multi-tenant database system (MTS). The term “multi-tenant database system” can refer to those systems in which various elements of hardware and software of a database system may be shared by one or more customers or tenants. For example, a given application server may simultaneously process requests for a large number of customers, and a given database table may store rows of data for a potentially much larger number of customers. A database image can include one or more database objects. A relational database management system (RDBMS) or the equivalent can execute storage and retrieval of information against the database object(s).

Application platform 18 can be a framework that allows the applications of database system 16 to execute, such as the hardware or software infrastructure of database system 16. In some implementations, application platform 18 enables the creation, management and execution of one or more applications developed by the provider of the on-demand database service, users accessing the on-demand database service via user systems 12, or third-party application developers accessing the on-demand database service via user systems 12.

In some embodiments, application platform 18 includes task processor 422, task pre-processor 418, task post-processor 420, web component service 424, priming records API 426, logging service 428, and cloud resources 430, as described herein. In an embodiment, cloud resources 430 described may represent resources related to processing device 17, tenant database 22, system database 24, and/or application platform 18.

In some implementations, database system 16 implements a web-based customer relationship management (CRM) system. For example, in some such implementations, database system 16 includes application servers configured to implement and execute CRM software applications as well as provide related data, code, forms, renderable web pages, and documents and other information to and from user systems 12 and to store to, and retrieve from, a database system related data, objects, and World Wide Web page content. In some MTS implementations, data for multiple tenants may be stored in the same physical database object in tenant database 22. In some such implementations, tenant data is arranged in the storage medium(s) of tenant database 22 so that data of one tenant is kept logically separate from that of other tenants so that one tenant does not have access to another tenant's data, unless such data is expressly shared. Database system 16 also implements applications other than, or in addition to, a CRM application. For example, database system 16 can provide tenant access to multiple hosted (standard and custom) applications, including a CRM application. User (or third-party developer) applications, which may or may not include CRM, may be supported by application platform 18. Application platform 18 manages the creation and storage of the applications into one or more database objects and the execution of the applications in one or more virtual machines in the process space of database system 16.

According to some implementations, each database system 16 is configured to provide web pages, forms, applications, data, and media content to user (client) systems 12 to support the access by user systems 12 as tenants of database system 16. As such, database system 16 provides security mechanisms to keep each tenant's data separate unless the data is shared. If more than one MTS is used, they may be located in close proximity to one another (for example, in a server farm located in a single building or campus), or they may be distributed at locations remote from one another (for example, one or more servers located in city A and one or more servers located in city B). As used herein, each MTS could include one or more logically or physically connected servers distributed locally or across one or more geographic locations. Additionally, the term “server” is meant to refer to a computing device or system, including processing hardware and process space(s), an associated storage medium such as a memory device or database, and, in some instances, a database application, such as an object-oriented database management system (OODBMS), a relational database management system (RDBMS), or an unstructured DB such as “noSQL” as is well known in the art. It should also be understood that “server system”, “server”, “server node”, and “node” are often used interchangeably herein. Similarly, the database objects described herein can be implemented as part of a single database, a distributed database, a collection of distributed databases, a database with redundant online or offline backups or other redundancies, etc., and can include a distributed database or storage network and associated processing intelligence.

Network 14 can be or include any network or combination of networks of systems or devices that communicate with one another. For example, network 14 can be or include any one or any combination of a local area network (LAN), wide area network (WAN), telephone network, wireless network, cellular network, point-to-point network, star network, token ring network, hub network, or other appropriate configuration. Network 14 can include a Transfer Control Protocol and Internet Protocol (TCP/IP) network, such as the global internetwork of networks often referred to as the “Internet” (with a capital “I”). The Internet will be used in many of the examples herein. However, it should be understood that the networks that the disclosed implementations can use are not so limited, although TCP/IP is a frequently implemented protocol.

User systems 12 (e.g., operated by customers) can communicate with database system 16 using TCP/IP and, at a higher network level, other common Internet protocols to communicate, such as the Hyper Text Transfer Protocol (HTTP), Hyper Text Transfer Protocol Secure (HTTPS), File Transfer Protocol (FTP), Apple File Service (AFS), Wireless Application Protocol (WAP), Secure Sockets layer (SSL) etc. In an example where HTTP is used, each user system 12 can include an HTTP client commonly referred to as a “web browser” or simply a “browser” for sending and receiving HTTP signals to and from an HTTP server of the database system 16. Such an HTTP server can be implemented as the sole network interface 20 between database system 16 and network 14, but other techniques can be used in addition to or instead of these techniques. In some implementations, network interface 20 between database system 16 and network 14 includes load sharing functionality, such as round-robin HTTP request distributors to balance loads and distribute incoming HTTP requests evenly over a number of servers. In MTS implementations, each of the servers can have access to the MTS data; however, other alternative configurations may be used instead.

User systems 12 can be implemented as any computing device(s) or other data processing apparatus or systems usable by users to access database system 16. For example, any of user systems 12 can be a desktop computer, a work station, a laptop computer, a tablet computer, a handheld computing device, a mobile cellular phone (for example, a “smartphone”), or any other Wi-Fi-enabled device, WAP-enabled device, or other computing device capable of interfacing directly or indirectly to the Internet or other network. When discussed in the context of a user, the terms “user system,” “user device,” and “user computing device” are used interchangeably herein with one another and with the term “computer.” As described above, each user system 12 typically executes an HTTP client, for example, a web browsing (or simply “browsing”) program, such as a web browser based on the WebKit platform, Microsoft's Internet Explorer browser, Netscape's Navigator browser, Opera's browser, Mozilla's Firefox browser, Google's Chrome browser, or a WAP-enabled browser in the case of a cellular phone, personal digital assistant (PDA), or other wireless device, allowing a user (for example, a subscriber of on-demand services provided by database system 16) of user system 12 to access, process, and view information, pages, and applications available to it from database system 16 over network 14.

Each user system 12 also typically includes one or more user input devices, such as a keyboard, a mouse, a trackball, a touch pad, a touch screen, a pen or stylus, or the like, for interacting with a graphical user interface (GUI) provided by the browser on a display (for example, a monitor screen, liquid crystal display (LCD), light-emitting diode (LED) display, etc.) of user system 12 in conjunction with pages, forms, applications, and other information provided by database system 16 or other systems or servers. For example, the user interface device can be used to access data and applications hosted database system 16, and to perform searches on stored data, or otherwise allow a user to interact with various GUI pages that may be presented to a user. As discussed above, implementations are suitable for use with the Internet, although other networks can be used instead of or in addition to the Internet, such as an intranet, an extranet, a virtual private network (VPN), a non-TCP/IP based network, any LAN or WAN or the like.

The users of user systems 12 may differ in their respective capacities, and the capacity of a particular user system 12 can be entirely determined by permissions (permission levels) for the current user of such user system. For example, where a salesperson is using a particular user system 12 to interact with database system 16, that user system can have the capacities allotted to the salesperson. However, while an administrator is using that user system 12 to interact with database system 16, that user system can have the capacities allotted to that administrator. Where a hierarchical role model is used, users at one permission level can have access to applications, data, and database information accessible by a lower permission level user, but may not have access to certain applications, database information, and data accessible by a user at a higher permission level. Thus, different users generally will have different capabilities with regard to accessing and modifying application and database information, depending on the users' respective security or permission levels (also referred to as “authorizations”).

According to some implementations, each user system 12 and some or all of its components are operator-configurable using applications, such as a browser, including computer code executed using a central processing unit (CPU), such as a Core® processor commercially available from Intel Corporation or the like. Similarly, database system 16 (and additional instances of an MTS, where more than one is present) and all of its components can be operator-configurable using application(s) including computer code to run using processing device 17, which may be implemented to include a CPU, which may include an Intel Core® processor or the like, or multiple CPUs. Each CPU may have multiple processing cores.

Database system 16 includes non-transitory computer-readable storage media having instructions stored thereon that are executable by or used to program a server or other computing system (or collection of such servers or computing systems) to perform some of the implementation of processes described herein. For example, program code 26 can include instructions for operating and configuring database system 16 to intercommunicate and to process web pages, applications, and other data and media content as described herein. In some implementations, program code 26 can be downloadable and stored on a hard disk, but the entire program code, or portions thereof, also can be stored in any other volatile or non-volatile memory medium or device as is well known, such as a read-only memory (ROM) or random-access memory (RAM), or provided on any media capable of storing program code, such as any type of rotating media including floppy disks, optical discs, digital video discs (DVDs), compact discs (CDs), micro-drives, magneto-optical discs, magnetic or optical cards, nanosystems (including molecular memory integrated circuits), or any other type of computer-readable medium or device suitable for storing instructions or data. Additionally, the entire program code, or portions thereof, may be transmitted and downloaded from a software source over a transmission medium, for example, over the Internet, or from another server, as is well known, or transmitted over any other existing network connection as is well known (for example, extranet, virtual private network (VPN), local area network (LAN), etc.) using any communication medium and protocols (for example, TCP/IP, HTTP, HTTPS, Ethernet, etc.) as are well known. It will also be appreciated that computer code for the disclosed implementations can be realized in any programming language that can be executed on a server or other computing system such as, for example, C, C++, HTML, any other markup language, Java™, JavaScript, ActiveX, any other scripting language, such as VBScript, and many other programming languages as are well known.

FIG. 1B illustrates a block diagram of example implementations of elements of FIG. 1A and example interconnections between these elements according to some implementations. That is, FIG. 1B also illustrates environment 10, but in FIG. 1B, various elements of database system 16 and various interconnections between such elements are shown with more specificity according to some more specific implementations. In some implementations, database system 16 may not have the same elements as those described herein or may have other elements instead of, or in addition to, those described herein.

In FIG. 1B, user system 12 includes a processor system 12A, a memory system 12B, an input system 12C, and an output system 12D. The processor system 12A can include any suitable combination of one or more processors. The memory system 12B can include any suitable combination of one or more memory devices. The input system 12C can include any suitable combination of input devices, such as one or more touchscreen interfaces, keyboards, mice, trackballs, scanners, cameras, or interfaces to networks. The output system 12D can include any suitable combination of output devices, such as one or more display devices, printers, or interfaces to networks.

In FIG. 1B, network interface 20 is implemented as a set of HTTP application servers 100 ₁-100 _(N). Each application server 100, also referred to herein as an “app server,” is configured to communicate with tenant database 22 and tenant data 23 stored therein, as well as system database 24 and system data 25 stored therein, to serve requests received from user systems 12. Tenant data 23 can be divided into individual tenant storage spaces 112, which can be physically or logically arranged or divided. Within each tenant storage space 112, tenant data 114 and application metadata 116 can similarly be allocated for each user. For example, a copy of a user's most recently used (MRU) items can be stored in tenant data 114. Similarly, a copy of MRU items for an entire organization that is a tenant can be stored to tenant space 112.

Database system 16 of FIG. 1B also includes a user interface (UI) 30 and an application programming interface (API) 32. Process space 28 includes system process space 102, individual tenant process spaces 104 and a tenant management process space 110. Application platform 18 includes an application setup mechanism 38 that supports application developers' creation and management of applications. Such applications and others can be saved as metadata into tenant database 22 by save routines 36 for execution by subscribers as one or more tenant process spaces 104 managed by tenant management process space 110, for example. Invocations to such applications can be coded using procedural language for structured query language (PL/SQL) 34, which provides a programming language style interface extension to the API 32. A detailed description of some PL/SQL language implementations is discussed in commonly assigned U.S. Pat. No. 7,730,478, titled METHOD AND SYSTEM FOR ALLOWING ACCESS TO DEVELOPED APPLICATIONS VIA A MULTI-TENANT ON-DEMAND DATABASE SERVICE, issued on Jun. 1, 2010, and hereby incorporated by reference herein in its entirety and for all purposes. Invocations to applications can be detected by one or more system processes, which manage retrieving application metadata 116 for the subscriber making the invocation and executing the metadata as an application in a virtual machine.

Each application server 100 can be communicably coupled with tenant database 22 and system database 24, for example, having access to tenant data 23 and system data 25, respectively, via a different network connection. For example, one application server 100 ₁ can be coupled via the network 14 (for example, the Internet), another application server 100 ₂ can be coupled via a direct network link, and another application server 100 _(N) can be coupled by yet a different network connection. Transfer Control Protocol and Internet Protocol (TCP/IP) are examples of typical protocols that can be used for communicating between application servers 100 and database system 16. However, it will be apparent to one skilled in the art that other transport protocols can be used to optimize database system 16 depending on the network interconnections used.

In some implementations, each application server 100 is configured to handle requests for any user associated with any organization that is a tenant of database system 16. Because it can be desirable to be able to add and remove application servers 100 from the server pool at any time and for various reasons, in some implementations there is no server affinity for a user or organization to a specific application server 100. In some such implementations, an interface system implementing a load balancing function as described herein with reference to FIGS. 4 and 5 is communicably coupled between application servers 100 and user systems 12 to distribute requests to application servers 100. In this manner, by way of example, database system 16 can be a multi-tenant system in which database system 16 handles storage of, and access to, different objects, data, and applications across disparate users and organizations.

In some embodiments, server 100 includes task processor 422, task pre-processor 418, task post-processor 420, web component service 424, priming records API 426, logging service 428, and cloud resources 430, as described herein.

In one example storage use case, one tenant can be a company that employs a sales force where each salesperson uses database system 16 to manage aspects of their sales. A user can maintain contact data, leads data, customer follow-up data, performance data, goals and progress data, etc., all applicable to that user's personal sales process (for example, in tenant database 22). In an example of a MTS arrangement, because all of the data and the applications to access, view, modify, report, transmit, calculate, etc., can be maintained and accessed by a user system 12 having little more than network access, the user can manage his or her sales efforts and cycles from any of many different user systems. For example, when a salesperson is visiting a customer and the customer has Internet access in their lobby, the salesperson can obtain critical updates regarding that customer while waiting for the customer to arrive in the lobby.

While each user's data can be stored separately from other users' data regardless of the employers of each user, some data can be organization-wide data shared or accessible by several users or all of the users for a given organization that is a tenant. Thus, there can be some data structures managed database system 16 that are allocated at the tenant level while other data structures can be managed at the user level. Because an MTS can support multiple tenants including possible competitors, the MTS can have security protocols that keep data, applications, and application use separate. Also, because many tenants may opt for access to an MTS rather than maintain their own system, redundancy, up-time, and backup are additional functions that can be implemented in the MTS. In addition to user-specific data and tenant-specific data, database system 16 also can maintain system level data usable by multiple tenants or other data. Such system level data can include industry reports, news, postings, and the like that are sharable among tenants.

In some implementations, user systems 12 (which also can be client systems) communicate with application servers 100 to request and update system-level and tenant-level data from database system 16. Such requests and updates can involve sending one or more queries to tenant database 22 or system database 24. Database system 16 (for example, an application server 100 in database system 16) can automatically generate one or more SQL statements (for example, one or more SQL queries) designed to access the desired information. System database 24 can generate query plans to access the requested data from the database. The term “query plan” generally refers to one or more operations used to access information in a database system.

Each database can generally be viewed as a collection of objects, such as a set of logical tables, containing data fitted into predefined or customizable categories. A “table” is one representation of a data object and may be used herein to simplify the conceptual description of objects and custom objects according to some implementations. It should be understood that “table” and “object” may be used interchangeably herein. Each table generally contains one or more data categories logically arranged as columns or fields in a viewable schema. Each row or element of a table can contain an instance of data for each category defined by the fields. For example, a CRM database can include a table that describes a customer with fields for basic contact information such as name, address, phone number, fax number, etc. Another table can describe a purchase order, including fields for information such as customer, product, sale price, date, etc. In some MTS implementations, standard entity tables can be provided for use by all tenants. For CRM database applications, such standard entities can include tables for case, account, contact, lead, and opportunity data objects, each containing pre-defined fields. As used herein, the term “entity” also may be used interchangeably with “object” and “table.”

In some MTS implementations, tenants are allowed to create and store custom objects, or may be allowed to customize standard entities or objects, for example by creating custom fields for standard objects, including custom index fields. Commonly assigned U.S. Pat. No. 7,779,039, titled CUSTOM ENTITIES AND FIELDS IN A MULTI-TENANT DATABASE SYSTEM, issued on Aug. 17, 2010, and hereby incorporated by reference herein in its entirety and for all purposes, teaches systems and methods for creating custom objects as well as customizing standard objects in a multi-tenant database system. In some implementations, for example, all custom entity data rows are stored in a single multi-tenant physical table, which may contain multiple logical tables per organization. It is transparent to customers that their multiple “tables” are in fact stored in one large table or that their data may be stored in the same table as the data of other customers.

FIG. 2A shows a system diagram illustrating example architectural components of an on-demand database service environment 200 according to some implementations. A client machine communicably connected with the cloud 204, generally referring to one or more networks in combination, as described herein, can communicate with the on-demand database service environment 200 via one or more edge routers 208 and 212. A client machine can be any of the examples of user systems 12 described above. The edge routers can communicate with one or more core switches 220 and 224 through a firewall 216. The core switches can communicate with a load balancer 228 (to perform actions as described below with reference to FIGS. 4 and 5), which can distribute server load over different pods, such as the pods 240 and 244. Pods 240 and 244, which can each include one or more servers or other computing resources, can perform data processing and other operations used to provide on-demand services. Communication with the pods can be conducted via pod switches 232 and 236. Components of the on-demand database service environment can communicate with database storage 256 through a database firewall 248 and a database switch 252.

As shown in FIGS. 2A and 2B, accessing an on-demand database service environment can involve communications transmitted among a variety of different hardware or software components. Further, the on-demand database service environment 200 is a simplified representation of an actual on-demand database service environment. For example, while only one or two devices of each type are shown in FIGS. 2A and 2B, some implementations of an on-demand database service environment can include anywhere from one to many devices of each type. Also, the on-demand database service environment need not include each device shown in FIGS. 2A and 2B or can include additional devices not shown in FIGS. 2A and 2B.

Additionally, it should be appreciated that one or more of the devices in the on-demand database service environment 200 can be implemented on the same physical device or on different hardware. Some devices can be implemented using hardware or a combination of hardware and software. Thus, terms such as “data processing apparatus,” “machine,” “server,” “device,” and “processing device” as used herein are not limited to a single hardware device; rather, references to these terms can include any suitable combination of hardware and software configured to provide the described functionality.

Cloud 204 is intended to refer to a data network or multiple data networks, often including the Internet. Client machines communicably connected with cloud 204 can communicate with other components of the on-demand database service environment 200 to access services provided by the on-demand database service environment. For example, client machines can access the on-demand database service environment to retrieve, store, edit, or process information. In some implementations, edge routers 208 and 212 route packets between cloud 204 and other components of the on-demand database service environment 200. For example, edge routers 208 and 212 can employ the Border Gateway Protocol (BGP). The BGP is the core routing protocol of the Internet. Edge routers 208 and 212 can maintain a table of Internet Protocol (IP) networks or ‘prefixes,’ which designate network reachability among autonomous systems on the Internet.

In some implementations, firewall 216 can protect the inner components of the on-demand database service environment 200 from Internet traffic. Firewall 216 can block, permit, or deny access to the inner components of on-demand database service environment 200 based upon a set of rules and other criteria. Firewall 216 can act as one or more of a packet filter, an application gateway, a stateful filter, a proxy server, or any other type of firewall.

In some implementations, core switches 220 and 224 are high-capacity switches that transfer packets within the on-demand database service environment 200. Core switches 220 and 224 can be configured as network bridges that quickly route data between different components within the on-demand database service environment. In some implementations, the use of two or more core switches 220 and 224 can provide redundancy or reduced latency.

In some implementations, pods 240 and 244 perform the core data processing and service functions provided by the on-demand database service environment. Each pod can include various types of hardware or software computing resources. An example of the pod architecture is discussed in greater detail with reference to FIG. 2B. In some implementations, communication between pods 240 and 244 is conducted via pod switches 232 and 236. Pod switches 232 and 236 can facilitate communication between pods 240 and 244 and client machines communicably connected with cloud 204, for example, via core switches 220 and 224. Also, pod switches 232 and 236 may facilitate communication between pods 240 and 244 and database storage 256. In some implementations, load balancer 228 can distribute workload between pods 240 and 244 as described below with reference to FIGS. 4 and 5. Balancing the on-demand service requests between the pods can assist in improving the use of resources, increasing throughput, reducing response times, or reducing overhead. Load balancer 228 may include multilayer switches to analyze and forward traffic.

In some implementations, access to database storage 256 is guarded by a database firewall 248. Database firewall 248 can act as a computer application firewall operating at the database application layer of a protocol stack. Database firewall 248 can protect database storage 256 from application attacks such as SQL injection, database rootkits, and unauthorized information disclosure. In some implementations, database firewall 248 includes a host using one or more forms of reverse proxy services to proxy traffic before passing it to a gateway router. Database firewall 248 can inspect the contents of database traffic and block certain content or database requests. Database firewall 248 can work on the SQL application level atop the TCP/IP stack, managing applications' connection to the database or SQL management interfaces as well as intercepting and enforcing packets traveling to or from a database network or application interface.

In some implementations, communication with database storage 256 is conducted via database switch 252. Multi-tenant database storage 256 can include more than one hardware or software components for handling database queries. Accordingly, database switch 252 can direct database queries transmitted by other components of the on-demand database service environment (for example, pods 240 and 244) to the correct components within database storage 256. In some implementations, database storage 256 is an on-demand database system shared by many different organizations as described above with reference to FIGS. 1A and 1B.

FIG. 2B shows a system diagram further illustrating example architectural components of an on-demand database service environment according to some implementations. Pod 244 can be used to render services to a user of on-demand database service environment 200. In some implementations, each pod includes a variety of servers or other systems. Pod 244 includes one or more content batch servers 264, content search servers 268, query servers 282, file servers 286, access control system (ACS) servers 280, batch servers 284, and app servers 288. Pod 244 also can include database instances 290, quick file systems (QFS) 292, and indexers 294. In some implementations, some or all communication between the servers in pod 244 can be transmitted via pod switch 236.

In some implementations, app servers 288 include a hardware or software framework dedicated to the execution of procedures (for example, programs, routines, scripts) for supporting the construction of applications provided by on-demand database service environment 200 via pod 244. In some implementations, the hardware or software framework of an app server 288 is configured to execute operations of the services described herein, including performance of the blocks of various methods or processes described herein. In some alternative implementations, two or more app servers 288 can be included and cooperate to perform such methods, or one or more other servers described herein can be configured to perform the disclosed methods.

Content batch servers 264 can handle requests internal to the pod. Some such requests can be long-running or not tied to a particular customer. For example, content batch servers 264 can handle requests related to log mining, cleanup work, and maintenance tasks. Content search servers 268 can provide query and indexer functions. For example, the functions provided by content search servers 268 can allow users to search through content stored in the on-demand database service environment. File servers 286 can manage requests for information stored in file storage 298. File storage 298 can store information such as documents, images, and binary large objects (BLOBs). In some embodiments, file storage 298 is a shared storage. By managing requests for information using file servers 286, the image footprint on the database can be reduced. Query servers 282 can be used to retrieve information from one or more file systems. For example, query servers 282 can receive requests for information from app servers 288 and transmit information queries to network file systems (NFS) 296 located outside the pod.

Pod 244 can share a database instance 290 configured as a multi-tenant environment in which different organizations share access to the same database. Additionally, services rendered by pod 244 may call upon various hardware or software resources. In some implementations, ACS servers 280 control access to data, hardware resources, or software resources. In some implementations, batch servers 284 process batch jobs, which are used to run tasks at specified times. For example, batch servers 284 can transmit instructions to other servers, such as app servers 288, to trigger the batch jobs.

In some implementations, QFS 292 is an open source file system available from Sun Microsystems, Inc. The QFS can serve as a rapid-access file system for storing and accessing information available within the pod 244. QFS 292 can support some volume management capabilities, allowing many disks to be grouped together into a file system. File system metadata can be kept on a separate set of disks, which can be useful for streaming applications where long disk seeks cannot be tolerated. Thus, the QFS system can communicate with one or more content search servers 268 or indexers 294 to identify, retrieve, move, or update data stored in NFS 296 or other storage systems.

In some implementations, one or more query servers 282 communicate with the NFS 296 to retrieve or update information stored outside of the pod 244. NFS 296 can allow servers located in pod 244 to access information to access files over a network in a manner similar to how local storage is accessed. In some implementations, queries from query servers 282 are transmitted to NFS 296 via load balancer 228, which can distribute resource requests over various resources available in the on-demand database service environment according to embodiments described below in FIGS. 4 and 5. NFS 296 also can communicate with QFS 292 to update the information stored on NFS 296 or to provide information to QFS 292 for use by servers located within pod 244.

In some implementations, the pod includes one or more database instances 290. Database instance 290 can transmit information to QFS 292. When information is transmitted to the QFS, it can be available for use by servers within pod 244 without using an additional database call. In some implementations, database information is transmitted to indexer 294. Indexer 294 can provide an index of information available in database instance 290 or QFS 292. The index information can be provided to file servers 286 or QFS 292. In some embodiments, there may be a plurality of database instances stored and accessed throughout the system.

FIG. 3 illustrates a diagrammatic representation of a machine in the exemplary form of a computer system 300 within which a set of instructions (e.g., for causing the machine to perform any one or more of the methodologies discussed herein) may be executed. In alternative implementations, the machine may be connected (e.g., networked) to other machines in a LAN, a WAN, an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server or a client machine in client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a PDA, a cellular telephone, a web appliance, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. Some or all of the components of the computer system 300 may be utilized by or illustrative of any of the electronic components described herein (e.g., any of the components illustrated in or described with respect to FIGS. 1A, 1B, 2A, and 2B).

The exemplary computer system 300 includes a processing device (processor) 302, a main memory 304 (e.g., ROM, flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 306 (e.g., flash memory, static random access memory (SRAM), etc.), and a data storage device 320, which communicate with each other via a bus 310.

Processor 302 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, processor 302 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. Processor 302 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. Processor 302 is configured to execute instructions 326 for performing the operations and steps discussed herein. Processor 302 may have one or more processing cores.

Computer system 300 may further include a network interface device 308. Computer system 300 also may include a video display unit 312 (e.g., a liquid crystal display (LCD), a cathode ray tube (CRT), or a touch screen), an alphanumeric input device 314 (e.g., a keyboard), a cursor control device 316 (e.g., a mouse or touch screen), and a signal generation device 322 (e.g., a loud speaker).

Power device 318 may monitor a power level of a battery used to power computer system 300 or one or more of its components. Power device 318 may provide one or more interfaces to provide an indication of a power level, a time window remaining prior to shutdown of computer system 300 or one or more of its components, a power consumption rate, an indicator of whether computer system is utilizing an external power source or battery power, and other power related information. In some implementations, indications related to power device 318 may be accessible remotely (e.g., accessible to a remote back-up management module via a network connection). In some implementations, a battery utilized by power device 318 may be an uninterruptable power supply (UPS) local to or remote from computer system 300. In such implementations, power device 318 may provide information about a power level of the UPS.

Data storage device 320 may include a tangible computer-readable storage medium 324 (e.g., a non-transitory computer-readable storage medium) on which is stored one or more sets of instructions 326 (e.g., software) embodying any one or more of the methodologies or functions described herein. Instructions 326 may also reside, completely or at least partially, within main memory 304 and/or within processor 302 during execution thereof by computer system 300, main memory 304, and processor 302 also constituting computer-readable storage media. Instructions 326 may further be transmitted or received over a network 330 (e.g., network 14) via network interface device 308.

In one implementation, instructions 326 include instructions for performing any of the implementations of mobile client application 402 and/or cloud computing environment 404 as described herein. While computer-readable storage medium 324 is shown in an exemplary implementation to be a single medium, it is to be understood that computer-readable storage medium 324 may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. In an embodiment, load balancer 404, metrics collector 436, and PID controller 440 run as independent processes (which could be in one or more processors 302, or different computing systems).

FIG. 4 is a diagram of an example system 404 for incremental application programming interface (API) processing based on resource utilization according to some embodiments. In one embodiment, client application 402 includes service processor 406, offline utility component 408, web component resolver 410, web container 412, priming client 414, and client database (DB) 416. In one embodiment, client application is running on a mobile computing device, such as a smart phone or a tablet computer. Client application 402 communicates with cloud computing environment 404 over a network, such as the Internet (not shown in FIG. 4). Server processor 406 manages the performance of services needed by the client application, including requests for data, such as web component definitions, images, and configurations, from cloud computing environment 404. Offline utility component 408 retrieves and stores data from client DB 416, including web component definitions, records from a table, and so on.

In an embodiment, every user interface (UI) component has an associated definition, where the event handling, styling, and rendering are defined. Offline utility component 408 requests these definitions from a server component 405 in cloud computing environment 404 and stores web component definition in client DB 416 for rendering the components even when the device running client application 402 is offline.

Here is an example pseudocode representation of a web component definition:

{ componentName: drop-down, Markup:<select name=“lightning-drop-down” id=“lightning-drop- down001” ><options>select1</options></select> Style:<style> . . . . </style> Event: <script></script> }

Web component resolver 410 resolves web component dependency definitions and ensures that the dependencies are available in a proactive manner, based on a web component dependency graph. Web component definitions represent hyper-text markup language (HTML)/Javascript definitions detailing how to render and handle events of an HTML UI component. The web component dependency definitions are required to display visual components in a hybrid mobile application screen (not shown in FIG. 4) (e.g., a drop down box, a multi-select pick list, user interface icons with custom branding, etc.).

Sometimes the component definitions are nested (e.g., represented in a tree structure) and web component resolver 410 takes care of traversing through the tree to ensure all direct and indirect definitions are retrieved. For example, take the case of a web page which includes user details such as (1) name, (2) address, (3) email and (4) phone details. The UI may have the name, email and phone as separate text input fields, whereas the address component can be a complex custom component having a text field for the address, a text field with number and length validation for a zip code, and a drop-down list structure with fixed sets of values for the state. Here, each of the text fields/drop-down list structures may be represented with separate definitions, the address component will be relying on those definitions and adds additional logic to the composed component. When the definition of the web page is provided, the requisite sub-component definitions will be either directly or indirectly derived from the page component. In this case it will be the address, drop-down list, text field, zip code field component, state-code field component, etc. In short when offline utility component 408 determines that it needs components Cmp1, Cmp2, Cmp3 to render page Pgl, and where each component is composed as a web dependency graph wherein:

-   -   Cmp1=depends on=>Cmp10=depends on=>Cmp101     -   Cmp2=depends on=>Cmp20=depends on=>Cmp202         Web component resolver 410 ensures all the definitions (Cmp1,         Cmp10, Cmp101, Cmp2, Cmp20, Cmp202) are retrieved by traversing         the dependencies from parent to child.

Web container 412 (similar to a web browser within a mobile application) is where the client application is rendered. In a hybrid mobile native application (e.g., using Android or Apple iOS), it is possible to render web components (e.g. HTML/Javascript). The rendering is usually done within a browser-like visual container that is presented by the mobile native application (such as client application 402) whenever there is a need to show web components. One advantage of having this hybrid design is to have a web component defined once and reused across multiple mobile platforms (e.g., Android/iOS/Windows).

Priming client 414 retrieves all needed resources from cloud computing environment 404 for offline capabilities of the client application. Priming client 414 is the consumer in client application 402 which calls priming records API 426 in cloud computing environment 404 to retrieve requested data “from the cloud.” Client DB 416 stores any data needed by client application 402. Client DB 416 stores all the record details, authentication details, web component definitions, etc. For example, when the mobile client application 402 is offline, some account pages can still be rendered in the mobile client application, since some of the account details like Account Name, Annual Revenue, Billing Address, Description, Industry, and Owner ID are stored in the database. Also, the page rendering details (e.g., web components) such as page sections (e.g., header, footer, details) are also stored in the client DB. Client DB 416 facilitates storage of all mobile application data needs and avoids fetching each time from server components in cloud computing environment 404.

In one embodiment, cloud computing environment 404 includes at least one server 405 including one or more of task pre-processor 418, task post-processor 420, task processor 422, web component service 424, priming records API 426, and logging service 428. Task processor 422 processes each of the individual tasks needed to service a user request received from client application 402. In an embodiment, a task could be a SQL query to a database (including cloud resources 430), or other similar process. In one example, a task could be another sequential API call to fetch additional details, or a synchronous queueing operation, where the response from the queue is needed to continue with the task. Task pre-processor 418 executes “pre-processes” life cycle tasks related to processing of user requests before task processor 422 runs. For example, this may include performing validation operations or input data transformation operations prior to executing primary tasks by task processor 422. Task post-processor 420 executes “post-processor” life cycle tasks. For example, this may include performing any post-query validation operations, output data transformation operations after task processor 422 runs. Web component service 424 comprises an API client which receives web component requests from service processor 406 of client application 402. In an embodiment, priming client 414 only receives record details (specific Accounts, Contacts, Opportunities, etc.), whereas web component service 424 contains details about the UI component itself, such as page details, header, details, sub-components etc.

Web component service 424 communicates with task processor 422 to fulfill user requests. Priming records API 426 queries cloud resources 430 as needed and generates results in response to user requests. In an embodiment, priming records API 426 calls task pre-processor 418, task processor 422, and task post-processor 420 as needed to perform tasks necessary to generate the results.

Cloud resources 430 include any computing resource available on cloud computing environment 404 for use in the fulfilment of a user request. In an embodiment, cloud resources 430 includes connections to databases, connections to message queues (MQs), and other server-based resources. In an embodiment, access to cloud infrastructure data including database, MQs, and logging, are pooled at the server level. Each thread that is allocated to the API processing will be sharing this common resource pool. This is a common resource pooling concept that is prevalent in most enterprise servers. In one embodiment, the request includes access to database (DB) operations, however in other embodiments, other server-side operations may be required, based on the user request. For example, a task could include any report generation processing that needs to be sent to list of email addresses; this can be achieved by posting a request in a messaging queue indicating details about the report.

Logging service 428 performs instrumentation and audit logging documenting how priming records API 426 and the other components described above in cloud computing environment 404 service user requests received from client application 402.

FIG. 5 is a flow diagram 500 of incremental API processing based on resource utilization according to some embodiments. First, priming client 414 of client application 402 makes a request to obtain data from cloud computing environment 404. In one example, this might be a request to fetch all the data sets matching the criteria: (1) all sales opportunities that have a Close Date (Due) in the next one month; (2) all sales accounts where the annual revenue is greater than $1 million; and (3) all contacts that belong to the accounts generated by processing query (2). Of course, this is just an example and the request may include fetching any set of data. At block 502, the request is received by priming records API 426. At block 504, priming records API 426 starts an API timer. The API timer is used to measure the elapsed time in processing of the request by cloud computing environment 404. At block 506, priming records API 426 collects one or more tasks that need to be executed in order to implement the request. For example, the tasks may include: (1) perform a DB operation to fetch accounts with revenue more than $1 million; and (2) publish a message to multiple client applications through a MQ (e.g., publish that an account is changed as a message to all interested sub-systems which monitor account level changes). In one embodiment, processing of a task includes performing a read operation to fetch selected data from a database in cloud resources 430. In an embodiment, priming records API 426 checks cloud resources 430 for relevant and available database, message queue, and/or configuration data in cloud computing environment 404.

At block 508, priming records API 426 checks if the current processing being performed is a relay continuation. A relay refers to a single request from the mobile client application 402 to the server 404, which does not completely fulfill the mobile client application needs. In some circumstances, multiple relays are required between the mobile client application and the server components in cloud computing environment 404 to fulfill the user request requirements. In some prior systems, the server fulfilled the mobile client application requests in a single server call with a monolithic and potentially huge payload. In an embodiment, processing of any request may be broken up into increments, where each increment is processed individually. In an embodiment, a relay may be identified by a relay token having a bookmark value (e.g., indicating how much of the request has been processed so far). A relay token acts like a bookmark for the API to know from where request processing should commence in multi-relay (server) requests.

For example, mobile client application 402 makes a request to collect records belonging to entities: (1) account; (2) contacts; and (3) opportunities. During the processing of accounts, consider a case when the threshold time set at the server side (e.g., by priming records API 426) is exceeded (e.g., it is taking too long to process the entire request). In this case, a response containing only account records may be sent to the mobile client application with a relay token as below:

Request 1: request from mobile client application, with relay token=0.

Response 1: Response for first request from mobile client application.  {   “primingRecords”: {   timestamp: 1587709454   relay_key: relayID1   “Account”: {     },  }  Request 2: request from mobile client, with relay token = relayID1 Response 2: Response for second request from mobile client application.  {   “primingRecords”: {   timestamp: 1587709454   relay_key: relayID1   “Contact”: {     },   “Opportunities”: {      },  }

If this is not a continuation of processing of multiple increments of the request (e.g., not a relay continuation, but the handling of a request in a single instance), then at block 510 priming records 426 processes each (unprocessed) task of the task list one at a time to perform the entire request. Priming records API 426 also updates a payload size of the payload to be returned to the client application (the payload including the results of performance of the request) and the elapsed time for processing the request. If this is a continuation of processing of multiple increments of the request (e.g., this is a relay), then at block 512 priming records API 426 continues to implement the request by starting continued processing of the request from where the processing was left off from a previous request. In an embodiment, one purpose of the relay is to break up a request from a single client into smaller manageable chunks, which are spread across multiple server calls from the same client. Since the request is broken down across multiple server calls, it is necessary for the client application to send the relay token in all subsequent server calls, to remind the server from where processing of the request should continue. Two different clients making server calls would use two different relay tokens.

Priming records API 426 discards tasks that were already previously processed during previous relays. For example, if a mobile client application makes a server request to fetch records from entities: (1) account; (2) contacts; and (3) opportunities, if when processing relay request 1, if the account result was already returned to the mobile client application, the second request will discard the results of processing the account portion of the request from being sent again. The current processing will be done only for contacts and opportunities, either both or just one entity may be processed in the second request based on available resources.

In an embodiment, information describing what tasks have already been processed are stored in a database in cloud resources 430 and may be retrieved from the database by reference to the relay token and associated bookmark value (which are sent in each relay request). A first request (not needing a continuation) does not have a relay token; only subsequent requests that are increments of the entire request will have a relay token. In either case, processing continues at block 514.

At block 514, if there are no more tasks to perform for handling the request, then at block 522 priming records API 426 prepares the payload containing the results from processing all tasks from the task list. An example of a payload (in JSON format) for a sample multi-leg relay response is shown below.

Response 1: Response for first request from mobile client application 402.  {   “primingRecords”: {    timestamp: 1587709454    relay_key: hashId for response1   “Account”: { // List of recordId's },   “Contact”: { // List of recordId's },   “Lead”: { // List of recordId's },   “Opportunity”: { // List of recordId's }  }  Response 2: Response for second request from mobile client  application 402.  {   “primingRecords”: {    timestamp: 1587709977  relay_key: hashId_for_response2   “WorkOrder”: { // List of recordId's },   “ServiceAppointment”: { // List of recordId's }  }

In an embodiment, the payload is “de-duped” to remove any duplicate data. Since all tasks have been performed for the request, there will be no more relays for handling this request. At block 524, priming records API 426 sends the payload to priming client 414 of client application 402 to complete request processing by cloud computing environment 404. Client application then consumes the payload as requested by the user. For example, this might result in the display of the payload information visually to the user.

At block 514, if there are more tasks to perform for handling the request, then at block 516 priming records API 426 checks if the elapsed time (e.g., process time) from the start of handling the request (started at block 504) exceeds a first predetermined threshold. If the process time has not been exceeded, then at block 518 priming records API 426 checks if the payload size storing the results of the processing the request exceeds a second predetermined threshold. If the payload size has not been exceeded, then priming records API 426 continues processing at block 512 with processing of additional tasks. If at block 516 the process time exceeds the first predetermined threshold or at block 518 the payload size exceeds the second predetermined threshold, then at block 520 priming records API 426 adds a relay token with a bookmark value to the payload, task processing is terminated, and processing continues with block 522. In an embodiment, the first threshold and the second threshold are set and continually adjust based at least in part on current operating parameters of a server running one or more of task pre-processor 418, task post-processor 420, task processor 422, web component service 424, priming records API 426, logging service 428, and cloud resources 430 in a cloud computing environment 404.

Examples of systems, apparatuses, computer-readable storage media, and methods according to the disclosed implementations are described in this section. These examples are being provided solely to add context and aid in the understanding of the disclosed implementations. It will thus be apparent to one skilled in the art that the disclosed implementations may be practiced without some or all the specific details provided. In other instances, certain process or method operations, also referred to herein as “blocks,” have not been described in detail in order to avoid unnecessarily obscuring the disclosed implementations. Other implementations and applications also are possible, and as such, the following examples should not be taken as definitive or limiting either in scope or setting.

In the detailed description, references are made to the accompanying drawings, which form a part of the description and in which are shown, by way of illustration, specific implementations. Although these disclosed implementations are described in sufficient detail to enable one skilled in the art to practice the implementations, it is to be understood that these examples are not limiting, such that other implementations may be used and changes may be made to the disclosed implementations without departing from their spirit and scope. For example, the blocks of the methods shown and described herein are not necessarily performed in the order indicated in some other implementations. Additionally, in some other implementations, the disclosed methods may include more or fewer blocks than are described. As another example, some blocks described herein as separate blocks may be combined in some other implementations. Conversely, what may be described herein as a single block may be implemented in multiple blocks in some other implementations. Additionally, the conjunction “or” is intended herein in the inclusive sense where appropriate unless otherwise indicated; that is, the phrase “A, B, or C” is intended to include the possibilities of “A,” “B,” “C,” “A and B,” “B and C,” “A and C,” and “A, B, and C.”

The words “example” or “exemplary” are used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the words “example” or “exemplary” is intended to present concepts in a concrete fashion.

In addition, the articles “a” and “an” as used herein and in the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. Reference throughout this specification to “an implementation,” “one implementation,” “some implementations,” or “certain implementations” indicates that a particular feature, structure, or characteristic described in connection with the implementation is included in at least one implementation. Thus, the appearances of the phrase “an implementation,” “one implementation,” “some implementations,” or “certain implementations” in various locations throughout this specification are not necessarily all referring to the same implementation.

Some portions of the detailed description may be presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the manner used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is herein, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “receiving,” “retrieving,” “transmitting,” “computing,” “generating,” “adding,” “subtracting,” “multiplying,” “dividing,” “optimizing,” “calibrating,” “detecting,” “performing,” “analyzing,” “determining,” “enabling,” “identifying,” “modifying,” “transforming,” “applying,” “aggregating,” “extracting,” “registering,” “querying,” “populating,” “hydrating,” “updating,” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission, or display devices.

The specific details of the specific aspects of implementations disclosed herein may be combined in any suitable manner without departing from the spirit and scope of the disclosed implementations. However, other implementations may be directed to specific implementations relating to each individual aspect, or specific combinations of these individual aspects. Additionally, while the disclosed examples are often described herein with reference to an implementation in which a computing environment is implemented in a system having an application server providing a front end for an on-demand database service capable of supporting multiple tenants, the present implementations are not limited to multi-tenant databases or deployment on application servers. Implementations may be practiced using other database architectures, i.e., ORACLE®, DB2® by IBM, and the like without departing from the scope of the implementations claimed. Moreover, the implementations are applicable to other systems and environments including, but not limited to, client-server models, mobile technology and devices, wearable devices, and on-demand services.

It should also be understood that some of the disclosed implementations can be embodied in the form of various types of hardware, software, firmware, or combinations thereof, including in the form of control logic, and using such hardware or software in a modular or integrated manner. Other ways or methods are possible using hardware and a combination of hardware and software. Any of the software components or functions described in this application can be implemented as software code to be executed by one or more processors using any suitable computer language such as, for example, C, C++, Java™, or Python using, for example, existing or object-oriented techniques. The software code can be stored as non-transitory instructions on any type of tangible computer-readable storage medium (referred to herein as a “non-transitory computer-readable storage medium”). Examples of suitable media include random access memory (RAM), read-only memory (ROM), magnetic media such as a hard-drive or a floppy disk, or an optical medium such as a compact disc (CD) or digital versatile disc (DVD), flash memory, and the like, or any combination of such storage or transmission devices. Computer-readable media encoded with the software/program code may be packaged with a compatible device or provided separately from other devices (for example, via Internet download). Any such computer-readable medium may reside on or within a single computing device or an entire computer system and may be among other computer-readable media within a system or network. A computer system, or other computing device, may include a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user.

The disclosure also relates to apparatuses, devices, and system adapted/configured to perform the operations herein. The apparatuses, devices, and systems may be specially constructed for their required purposes, may be selectively activated or reconfigured by a computer program, or some combination thereof.

In the foregoing description, numerous details are set forth. It will be apparent, however, to one of ordinary skill in the art having the benefit of this disclosure, that the present disclosure may be practiced without these specific details. While specific implementations have been described herein, it should be understood that they have been presented by way of example only, and not limitation. The breadth and scope of the present application should not be limited by any of the implementations described herein but should be defined only in accordance with the following and later-submitted claims and their equivalents. Indeed, other various implementations of and modifications to the present disclosure, in addition to those described herein, will be apparent to those of ordinary skill in the art from the foregoing description and accompanying drawings. Thus, such other implementations and modifications are intended to fall within the scope of the present disclosure.

Furthermore, although the present disclosure has been described herein in the context of a particular implementation in a particular environment for a particular purpose, those of ordinary skill in the art will recognize that its usefulness is not limited thereto and that the present disclosure may be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below should be construed in view of the full breadth and spirit of the present disclosure as described herein, along with the full scope of equivalents to which such claims are entitled. 

What is claimed is:
 1. A computer-implemented method comprising: receiving a request from a client application to obtain data; collecting a list of tasks to be performed to process the request to obtain the data; performing tasks from the list of tasks until an elapsed time to perform the list of tasks exceeds a first threshold and a size of a payload storing the data exceeds a second threshold; preparing the payload; and sending the payload to the client application.
 2. The computer-implemented method of claim 1, comprising adding a relay token to the payload indicating a partial payload resulting from partial processing of the request when the elapsed time to perform the list of tasks exceeds the first threshold and the size of the payload storing the data exceeds the second threshold.
 3. The computer-implemented method of claim 2, wherein the relay token indicates to the client application how much of the request has been processed in the partial payload.
 4. The computer-implemented method of claim 1, comprising continually adjusting the first threshold and the second threshold based at least in part on current operating parameters of a server in a cloud computing environment.
 5. The computer-implemented method of claim 1, comprising setting a timer to measure the elapsed time to perform the list of tasks when the request is received.
 6. The computer-implemented method of claim 1, wherein performing tasks from the list of tasks comprises performing one or more read operations to fetch selected data from a database.
 7. The computer-implemented method of claim 1, wherein performing tasks from the list of tasks comprises publishing one or messages to client applications through a message queue.
 8. A tangible, non-transitory computer-readable storage medium having instructions stored thereon which, when executed by a processing device, cause the processing device to: receive a request from a client application to obtain data; collect a list of tasks to be performed to process the request to obtain the data; perform tasks from the list of tasks until an elapsed time to perform the list of tasks exceeds a first threshold and a size of a payload storing the data exceeds a second threshold; prepare the payload; and send the payload to the client application.
 9. The tangible, non-transitory computer-readable storage medium of claim 8, comprising instructions to add a relay token to the payload indicating a partial payload resulting from partial processing of the request when the elapsed time to perform the list of tasks exceeds the first threshold and the size of the payload storing the data exceeds the second threshold.
 10. The tangible, non-transitory computer-readable storage medium of claim 9, wherein the relay token indicates to the client application how much of the request has been processed in the partial payload.
 11. The tangible, non-transitory computer-readable storage medium of claim 8, comprising instructions to continually adjust the first threshold and the second threshold based at least in part on current operating parameters of a server in a cloud computing environment.
 12. The tangible, non-transitory computer-readable storage medium of claim 8, wherein performing tasks from the list of tasks comprises performing one or more read operations to fetch selected data from a database.
 13. The tangible, non-transitory computer-readable storage medium of claim 8, wherein performing tasks from the list of tasks comprises publishing one or messages to client applications through a message queue.
 14. A cloud computing system comprising: cloud computing resources; and a server to receive a request from a client application to obtain data from the cloud computing resources; collect a list of tasks to be performed to process the request to obtain the data from the cloud computing resources; perform tasks from the list of tasks until an elapsed time to perform the list of tasks exceeds a first threshold and a size of a payload storing the data exceeds a second threshold; prepare the payload; and send the payload to the client application.
 15. The cloud computing system of claim 14, comprising the server to add a relay token to the payload indicating a partial payload resulting from partial processing of the request when the elapsed time to perform the list of tasks exceeds the first threshold and the size of the payload storing the data exceeds the second threshold.
 16. The cloud computing system of claim 15, wherein the relay token indicates to the client application how much of the request has been processed in the partial payload.
 17. The cloud computing system of claim 14, comprising the server to continually adjust the first threshold and the second threshold based at least in part on current operating parameters of a server in a cloud computing environment.
 18. The cloud computing system of claim 14, wherein the server to perform tasks from the list of tasks comprises the server to perform one or more read operations to fetch selected data from a database.
 19. The cloud computing system of claim 14, wherein the server to perform tasks from the list of tasks comprises the server to publish one or messages to client applications through a message queue.
 20. The cloud computing system of claim 14, wherein the cloud computing resources comprises at least one of a connection to a database and a connection to a message queue (MQ). 